Fragility Across Time

Loading Libraries

library(tidyverse)

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# install.packages("vtable")
library(vtable)

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library("Hmisc")

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# install.packages("corrplot")
library(corrplot)

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library(naniar)
library(corrplot)
library(stargazer)

## 
## Please cite as:

##  Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.

##  R package version 5.2.2. https://CRAN.R-project.org/package=stargazer

library(htmltools)

Loading Data

df <- read_csv("../../data/final/merged_data.csv")

## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   .default = col_double(),
##   iso = col_character(),
##   country_name = col_character(),
##   hdi_value = col_character()
## )
## ℹ Use `spec()` for the full column specifications.

adb <- read_csv("../../data/final/adb-members.csv")

## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   country_name = col_character(),
##   iso = col_character(),
##   region = col_character(),
##   donor = col_double(),
##   sids = col_double(),
##   ldc = col_double()
## )

output.fig.dir <- "../../output/figures"
output.tab.dir <- "../../output/tables"

SAVE.RESULTS = TRUE

df <- df %>% 
  rename(migrant_stock=ims_both_sex, 
         refugee_stock=estimated_refugee_stock_incl_asylum_seekers_both_sexes,
         disaster_displacement=disaster_stock_displacementr_raw,
         conflict_displacement=conflict_stock_displacement_raw, 
         climate_change=CCH, 
         air_quality=AIR, 
         rule_of_law=`value.Rule of Law: Estimate`, 
         gov_effectiveness=`value.Government Effectiveness: Estimate`, 
         corruption_control=`value.Control of Corruption: Estimate`,
         state_legit=`P1: State Legitimacy`, 
         cpa_d_12=D12, 
         cpa_d_avg=D_avg, 
         gdp=`GDP per capita (constant 2015 US$)`, 
         gini=`value.Gini index (World Bank estimate)`) %>% 
  mutate(conflict_displacement=conflict_displacement/10000, 
         disaster_displacement=disaster_displacement/10000,
         migrant_stock=migrant_stock/10000,
         refugee_stock=refugee_stock/10000,
         gdp=gdp/1000,
         state_legit=10-state_legit, 
         hdi_value=as.numeric(hdi_value))

## Warning in mask$eval_all_mutate(quo): NAs introduced by coercion

col_names <- c('migrant_stock'='Migrant Stock (10,000s)', 
               'refugee_stock'='Refugee Stock (10,000s)',
               'disaster_displacement'='Internal Displacement Due to Disasters (10,000s)',
               'conflict_displacement'='Internal Displacement Due to Conflict (10,000s)', 
               'climate_change'='Climate Change', 
               'air_quality'='Air Quality', 
               'rule_of_law'='Rule of Law', 
               'gov_effectiveness'='Government Effectiveness', 
               'corruption_control'='Control of Corruption',
               'state_legit'='State Legitimacy', 
               'cpa_d_12'='CPA: D-12', 
               'cpa_d_avg'='CPA: Cluster D Average', 
               'gdp'='GDP Per Capita (1,000s)', 
               'hdi_value'="HDI",
               'gini'="Gini Index") 

generic.cols <- c('iso', 'year', 'country_name', 'region', 'donor', 'sids', 'ldc')
outcome.cols <- c('state_legit', 'cpa_d_avg', 'cpa_d_12')

keep <- df %>% 
  arrange(year, iso) %>% 
  select(names(col_names)) %>% 
  mutate(keep = if_any(everything(), ~ !is.na(.))) %>% 
  pull(keep)

df <- df %>% 
  arrange(year, iso) %>% 
  select(iso, year, names(col_names)) %>% 
  arrange(year) %>% 
  filter(keep) %>% 
  left_join(adb, by='iso')

Dependent variables across time

With both donors and recipients, across regions

for (reg in unique(df$region)) {
  for (outcome in outcome.cols) {
    plt <- df %>% 
      filter(region==reg) %>% 
      select('iso', 'year', outcome) %>% 
      drop_na() %>% 
      ggplot(aes_string(x='year', outcome)) +
      geom_line(aes(color=iso)) + 
      geom_point(aes(color=iso)) +
      labs(title=reg, subtitle=paste(col_names[outcome], 'across years')) +
      theme_classic()
    print(plt)
    if (SAVE.RESULTS) {
      ggsave(paste(output.fig.dir, '/year_X_', outcome, '_', tolower(str_replace(reg, " " ,"-")), '.png', sep=''))
    }
  }
}

## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(outcome)` instead of `outcome` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.

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With only recipients, across regions

for (reg in unique(df$region)) {
  for (outcome in outcome.cols) {
    plt <- df %>% 
      filter(region==reg & donor==0) %>% 
      select('iso', 'year', outcome) %>% 
      drop_na() %>% 
      ggplot(aes_string(x='year', outcome)) +
      geom_line(aes(color=iso)) + 
      geom_point(aes(color=iso)) +
      labs(title=reg, subtitle=paste(col_names[outcome], 'across years')) +
      theme_classic()
    print(plt)
    if (SAVE.RESULTS) {
      ggsave(paste(output.fig.dir, '/year_X_', outcome, '_', tolower(str_replace(reg, " " ,"-")), '_recipients', '.png', sep=''))
    }
  }
}

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Across donor and recipient countries

for (status in c('Donors', 'Recipients')) {
  for (outcome in outcome.cols) {
    plt <- df %>% 
      mutate(donor=ifelse(donor, 'Donors', 'Recipients')) %>% 
      filter(donor==status) %>% 
      select('iso', 'year', outcome) %>% 
      drop_na() %>% 
      ggplot(aes_string(x='year', outcome)) +
      geom_line(aes(color=iso)) + 
      geom_point(aes(color=iso)) +
      labs(title=status, subtitle=paste(col_names[outcome], 'across years')) +
      theme_classic()
    print(plt)
    if (SAVE.RESULTS) {
      ggsave(paste(output.fig.dir, '/year_X_', outcome, '_', tolower(status), '.png', sep=''))
    }
  }
}

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Paired Scatterplots

## Note: Using an external vector in selections is ambiguous.
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## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.

## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(col)` instead of `col` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.

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## Warning in max(sdf$year): no non-missing arguments to max; returning -Inf

## Warning in min(sdf$year): no non-missing arguments to min; returning Inf

## Warning in max(sdf$year): no non-missing arguments to max; returning -Inf

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Fixed Effects Regression

https://www.princeton.edu/~otorres/Panel101R.pdf panel_model <- plm(state_leg ~ climate, data = data_set, indec = c(“iso”, “year”), model = “within”) Maya Van Nuys (she/her) to Everyone (1:31 PM) panel data code (updated): panel_model <- plm(state_leg ~ climate, data = data_set, index = c(“iso”, “year”), model = “within”) library(AER) library(plm)

panel_model <- plm(state_leg ~ climate, data = data_set, index = c(“iso”, “year”), model = “within”, effect = “twoways”)

library(AER)

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## Loading required package: carData

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library(plm)

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# plm(state_legit ~ air_quality + climate_change + hdi_value + gdp, data = df, index = c("iso", "year"), model = "within")

model <- plm(state_legit ~ air_quality + climate_change + hdi_value + gdp, data = df, index = c("iso", "year"), model = "within", effect = "twoways")

## Warning in pdata.frame(data, index): duplicate couples (id-time) in resulting pdata.frame
##  to find out which, use, e.g., table(index(your_pdataframe), useNA = "ifany")

summary(model)

## Twoways effects Within Model
## 
## Call:
## plm(formula = state_legit ~ air_quality + climate_change + hdi_value + 
##     gdp, data = df, effect = "twoways", model = "within", index = c("iso", 
##     "year"))
## 
## Unbalanced Panel: n = 38, T = 1-6, N = 177
## 
## Residuals:
##        Min.     1st Qu.      Median     3rd Qu.        Max. 
## -1.2872e+00 -3.4377e-01 -2.0157e-15  2.9639e-01  1.2152e+00 
## 
## Coefficients:
##                  Estimate Std. Error t-value Pr(>|t|)
## air_quality     0.0067652  0.0046772  1.4464   0.1505
## climate_change -0.0035863  0.0038556 -0.9301   0.3540
## hdi_value       8.2149749  6.3619997  1.2913   0.1989
## gdp            -0.0218387  0.0477644 -0.4572   0.6483
## 
## Total Sum of Squares:    44.579
## Residual Sum of Squares: 43.395
## R-Squared:      0.026561
## Adj. R-Squared: -0.31789
## F-statistic: 0.88679 on 4 and 130 DF, p-value: 0.47388